Dissimilar but relevant search engine results

ABSTRACT

A search engine is configured to return increased diversity results based on past user interactions with search results. For a given query, historical data is analyzed to generate an item score describing a past quantity of users that navigated to a given page of an item. The historical data can further be used to generate a category score describing a past quantity of users that navigated to a given category of items. The category of items can be analyzed to generate a diversity score describing their diversity with respect to each other. Results for the given query can be arranged using items scores, category scores, and diversity scores.

RELATED APPLICATIONS

This application claims the priority benefit, under 35 U.S.C. Section119(e), to U.S. Provisional Application No. 62/331,547, entitled “BroadQuery-Based Product and Category Ranking System,” filed May 4, 2016,which is hereby incorporated by reference in its entirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to search enginesand, more particularly, but not by way of limitation, to a search engineconfigured to return dissimilar but relevant search results.

BACKGROUND

An Internet-centric problem when searching online using search enginesis lack of diversity in search results. Often, many of the search enginereturned results are nearly identical to one another. These redundantresults can decrease the usefulness of some types of search engines.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

To easily identify the discussion of any particular element or act, themost significant digit or digits in a reference number refer to thefigure number in which that element is first introduced.

FIG. 1 is a block diagram illustrating a networked system implementing adiversity search engine, according to some example embodiments.

FIG. 2 is a block diagram showing the architectural details of adiversity search engine, according to some example embodiments.

FIG. 3 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 4 is a block diagram illustrating components of a machine,according to some example embodiments, able to read instructions from amachine-storage medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

FIG. 5 illustrates a flow diagram of a method for generating itemrankings, according to some example embodiments.

FIG. 6 illustrates a flow diagram of a method for altering rankingsbased at least in part on item category ranks, according to some exampleembodiments.

FIG. 7 illustrates a flow diagram of a method for altering rankingsbased at least in part on diversity information, according to someexample embodiments.

FIG. 8 is a user interface diagram that shows example results of thediversity search engine, according to some example embodiments.

DETAILED DESCRIPTION

The present disclosure seeks to provide technical solutions to the abovementioned problems by employing a diversity search engine to returnresults that are different from one another (e.g., have differentattributes, have different categorization) but are still relevant to agiven query. The diversity search engine seeks to leverage the structureof a database for items published to a listing website. It isappreciated that there are different types of search engines. Someconventional search engines rely heavily on keyword matching query termsto terms found in the results. These conventional search engines mayleave out results that users frequently navigate to when browsingthrough query results. Thus, the sought after (e.g., navigated to)results are left buried in second and third webpages of search resultsand in fact may never even be retrieved from the database.

According to some example approaches, the diversity search enginereceives a search request indication from the client device and accessessession data that relates to one or more historical user sessions. Thediversity search engine detects items having item pages that wereaccessed during one or more user sessions after submission of the searchquery in the one or more user sessions. The diversity search enginefurther detects item categories that contain items having pages accessedduring the one or more user sessions after the submission of the searchquery. The diversity search engine can further rank the items or theitem categories based on a score determined by the user sessions thatshow a client device navigated to the item (e.g., webpage displayingdata about the item) after submitting the search query. The ranking maybe based on various factors including an average time between thesubmission of the search query and navigation to an item page or to anitem category, filtering of the search query after submission of thesearch query but before navigation to the item page or the itemcategory, or a purchase of an item between submission of the searchquery and navigation to the item or the item category. The diversitysearch engine can further cause a presentation on the client device thatdisplays a selection of items and a selection of item categories basedon the ranking of the items in the item categories. The diversity searchengine can further generate the display of items and item categories inorder to present a diverse group of items or item categories in order topresent less homogenous results on the client device.

Glossary

“CARRIER SIGNAL” in this context refers to any intangible medium that iscapable of storing, encoding, or carrying instructions for execution bythe machine, and includes digital or analog communications signals orother intangible medium to facilitate communication of suchinstructions. Instructions may be transmitted or received over thenetwork using a transmission medium via a network interface device andusing any one of a number of well-known transfer protocols.

“CLIENT DEVICE” in this context refers to any machine that interfaces toa communications network to obtain resources from one or more serversystems or other client devices. A client device may be, but is notlimited to, a mobile phone, desktop computer, laptop, portable digitalassistants (PDAs), smart phones, tablets, ultra books, netbooks,laptops, multi-processor systems, microprocessor-based or programmableconsumer electronics, game consoles, set-top boxes, or any othercommunication device that a user may use to access a network.

“COMMUNICATIONS NETWORK” in this context refers to one or more portionsof a network that may be an ad hoc network, an intranet, an extranet, avirtual private network (VPN), a local area network (LAN), a wirelessLAN (WLAN), a wide area network (WAN), a wireless WAN (WWAN), ametropolitan area network (MAN), the Internet, a portion of theInternet, a portion of the Public Switched Telephone Network (PSTN), aplain old telephone service (POTS) network, a cellular telephonenetwork, a wireless network, a Wi-Fi® network, another type of network,or a combination of two or more such networks. For example, a network ora portion of a network may include a wireless or cellular network andthe coupling may be a Code Division Multiple Access (CDMA) connection, aGlobal System for Mobile communications (GSM) connection, or other typeof cellular or wireless coupling. In this example, the coupling mayimplement any of a variety of types of data transfer technology, such asSingle Carrier Radio Transmission Technology (1×RTT), Evolution-DataOptimized (EVDO) technology, General Packet Radio Service (GPRS)technology, Enhanced Data rates for GSM Evolution (EDGE) technology,third Generation Partnership Project (3GPP) including 3G, fourthgeneration wireless (4G) networks, Universal Mobile TelecommunicationsSystem (UMTS), High Speed Packet Access (HSPA), WorldwideInteroperability for Microwave Access (WiMAX), Long Term Evolution (LTE)standard, others defined by various standard setting organizations,other long range protocols, or other data transfer technology.

“COMPONENT” in this context refers to a device, physical entity, orlogic having boundaries defined by function or subroutine calls, branchpoints, application program interfaces (APIs), or other technologiesthat provide for the partitioning or modularization of particularprocessing or control functions. Components may be combined via theirinterfaces with other components to carry out a machine process. Acomponent may be a packaged functional hardware unit designed for usewith other components and a part of a program that usually performs aparticular function of related functions. Components may constituteeither software components (e.g., code embodied on a machine-readablemedium) or hardware components. A “hardware component” is a tangibleunit capable of performing certain operations and may be configured orarranged in a certain physical manner. In various example embodiments,one or more computer systems (e.g., a standalone computer system, aclient computer system, or a server computer system) or one or morehardware components of a computer system (e.g., a processor or a groupof processors) may be configured by software (e.g., an application orapplication portion) as a hardware component that operates to performcertain operations as described herein. A hardware component may also beimplemented mechanically, electronically, or any suitable combinationthereof. For example, a hardware component may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware component may be a special-purpose processor,such as a Field-Programmable Gate Array (FPGA) or an ApplicationSpecific Integrated Circuit (ASIC). A hardware component may alsoinclude programmable logic or circuitry that is temporarily configuredby software to perform certain operations. For example, a hardwarecomponent may include software executed by a general-purpose processoror other programmable processor. Once configured by such software,hardware components become specific machines (or specific components ofa machine) uniquely tailored to perform the configured functions and areno longer general-purpose processors. It will be appreciated that thedecision to implement a hardware component mechanically, in dedicatedand permanently configured circuitry, or in temporarily configuredcircuitry (e.g., configured by software) may be driven by cost and timeconsiderations. Accordingly, the phrase “hardware component” (or“hardware-implemented component”) should be understood to encompass atangible entity, be that an entity that is physically constructed,permanently configured (e.g., hardwired), or temporarily configured(e.g., programmed) to operate in a certain manner or to perform certainoperations described herein. Considering embodiments in which hardwarecomponents are temporarily configured (e.g., programmed), each of thehardware components need not be configured or instantiated at any oneinstance in time. For example, where a hardware component comprises ageneral-purpose processor configured by software to become aspecial-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware components) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware component at one instanceof time and to constitute a different hardware component at a differentinstance of time. Hardware components can provide information to, andreceive information from, other hardware components. Accordingly, thedescribed hardware components may be regarded as being communicativelycoupled. Where multiple hardware components exist contemporaneously,communications may be achieved through signal transmission (e.g., overappropriate circuits and buses) between or among two or more of thehardware components. In embodiments in which multiple hardwarecomponents are configured or instantiated at different times,communications between such hardware components may be achieved, forexample, through the storage and retrieval of information in memorystructures to which the multiple hardware components have access. Forexample, one hardware component may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware component may then, at alater time, access the memory device to retrieve and process the storedoutput. Hardware components may also initiate communications with inputor output devices, and can operate on a resource (e.g., a collection ofinformation). The various operations of example methods described hereinmay be performed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implementedcomponents that operate to perform one or more operations or functionsdescribed herein. As used herein, “processor-implemented component”refers to a hardware component implemented using one or more processors.Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented components. Moreover, the one or more processorsmay also operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)). The performance of certain of the operations may bedistributed among the processors, not only residing within a singlemachine, but deployed across a number of machines. In some exampleembodiments, the processors or processor-implemented components may belocated in a single geographic location (e.g., within a homeenvironment, an office environment, or a server farm). In other exampleembodiments, the processors or processor-implemented components may bedistributed across a number of geographic locations.

“MACHINE-STORAGE MEDIUM” in this context refers to a component, deviceor other tangible media able to store instructions and data temporarilyor permanently and may include, but is not be limited to, random-accessmemory (RAM), read-only memory (ROM), buffer memory, flash memory,optical media, magnetic media, cache memory, other types of storage(e.g., Erasable Programmable Read-Only Memory (EEPROM)) and/or anysuitable combination thereof. The term “machine-storage medium” shouldbe taken to include a single medium or multiple media (e.g., acentralized or distributed database, or associated caches and servers)able to store instructions. The term “machine-storage medium” shall alsobe taken to include any medium, or combination of multiple media, thatis capable of storing instructions (e.g., code) for execution by amachine, such that the instructions, when executed by one or moreprocessors of the machine, cause the machine to perform any one or moreof the methodologies described herein. Accordingly, a “machine-storagemedium” refers to a single storage apparatus or device, as well as“cloud-based” storage systems or storage networks that include multiplestorage apparatus or devices. The term “machine-storage medium” excludessignals per se.

“SIGNAL MEDIUM” The term “signal medium” or “transmission medium” shallbe taken to include any form of modulated data signal, carrier wave, andso forth. The term “modulated data signal” means a signal that has oneor more of its characteristics set or changed in such a matter as toencode information in the signal.

“COMPUTER READABLE MEDIUM The terms “machine-readable medium,”“computer-readable medium” and “device-readable medium” mean the samething and may be used interchangeably in this disclosure. The terms aredefined to include both machine-storage media and signal media. Thus,the terms include both storage devices/media and carrier waves/modulateddata signals.

The instructions may further be transmitted or received over acommunications network 626 using a transmission medium via the networkinterface device and utilizing any one of a number of well-knowntransfer protocols (e.g., HTTP). Examples of communication networksinclude a local area network (LAN), a wide area network (WAN), theInternet, mobile telephone networks, plain old telephone service (POTS)networks, and wireless data networks (e.g., WiFi, LTE, and WiMAXnetworks). The term “transmission medium” or “signal medium” shall betaken to include any intangible medium that is capable of storing,encoding, or carrying instructions for execution by the machine, andincludes digital or analog communications signals or other intangiblemedium to facilitate communication of such software.

“PROCESSOR” in this context refers to any circuit or virtual circuit (aphysical circuit emulated by logic executing on an actual processor)that manipulates data values according to control signals (e.g.,“commands,” “op codes,” “machine code,”) and which producescorresponding output signals that are applied to operate a machine. Aprocessor may, for example, be a Central Processing Unit (CPU), aReduced Instruction Set Computing (RISC) processor, a ComplexInstruction Set Computing (CISC) processor, a Graphics Processing Unit(GPU), a Digital Signal Processor (DSP), an Application SpecificIntegrated Circuit (ASIC), a Radio-Frequency Integrated Circuit (RFIC)or any combination thereof. A processor may further be a multi-coreprocessor having two or more independent processors (sometimes referredto as “cores”) that may execute instructions contemporaneously.

Description

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program items that embodyillustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

With reference to FIG. 1, an example embodiment of a high-level SaaSnetwork architecture 100 is shown. A networked system 116 providesserver-side functionality via a network 110 (e.g., the Internet or widearea network (WAN)) to a client device 108. A web client 102 and aprogrammatic client, in the example form of an application 104 arehosted and execute on the client device 108. The networked system 116includes an application server 122, which in turn hosts a publicationsystem 106 that provides a number of functions and services to theapplication 104 that accesses the networked system 116.

The client device 108 enables a user to access and interact with thenetworked system 116. For instance, the user provides input (e.g., touchscreen input or alphanumeric input) to the client device 108, and theinput is communicated to the networked system 116 via the network 110.In this instance, the networked system 116, in response to receiving theinput from the user, communicates information back to the client device108 via the network 110 to be presented to the user.

An Application Program Interface (API) server 118 and a web server 120are coupled to, and provide programmatic and web interfacesrespectively, to the application server 122. The application server 122hosts the publication system 106 which allows the publication of itemson the application server 122, and a diversity search engine 128, whichincludes components described herein. The diversity search engine 128 isconfigured to receive a query and return search results that aredissimilar to one another (e.g., categorically diverse, diverseattributes, diverse features), yet still relevant to the query accordingto past historical user data. Further details of the diversity searchengine 128 are discussed below with reference to FIG. 2. The applicationserver 122 is, in turn, shown to be coupled to a database server 124that facilitates access to information storage repositories (e.g., adatabase 126). In an example embodiment, the database 126 includesstorage devices that store information accessed and generated by thepublication system 106.

Additionally, a third party application 114, executing on a third partyserver 112, is shown as having programmatic access to the networkedsystem 116 via the programmatic interface provided by the ApplicationProgram Interface (API) server 118. For example, the third partyapplication 114, using information retrieved from the networked system116, may support one or more features or functions on a website hostedby the third party.

Turning now specifically to the applications hosted by the client device108, the web client 102 may access the various systems (e.g.,publication system 106) via the web interface supported by the webserver 120. Similarly, the application 104 (e.g., an “app”) accesses thevarious services and functions provided by the publication system 106via the programmatic interface provided by the Application ProgramInterface (API) server 118. The application 104 may, for example, be an“app” executing on the client device 108, such as an iOS or Android OSapplication to enable user to access and input data on the networkedsystem 116 in an off-line manner, and to perform batch-modecommunications between the application 104 and the networked system 116.

Further, while the SaaS network architecture 100 shown in FIG. 1 employsa client-server architecture, the present inventive subject matter is ofcourse not limited to such an architecture, and could equally well findapplication in a distributed, or peer-to-peer, architecture system, forexample. The publication system 106 could also be implemented as astandalone software program, which do not necessarily have networkingcapabilities.

FIG. 2 is a block diagram showing the architectural details of thediversity search engine 128, according to some example embodiments. Thediversity search engine 128 may be activated on a physical computer(e.g., server) activated by a power button 204, according to someexample embodiments. As illustrated, the diversity search engine 128 isincludes a communication engine 205 by which the diversity search engine128 communicates (e.g., over the network 110) with other systems withinthe SaaS network architecture 100. Additionally shown are an analysisengine 210, scoring engine 215, ranking engine 220, and display engine225, which are all communicatively coupled to communication engine 205within the diversity search engine 128.

The communication engine 205 operates to receive information from theclient device 108 or access information from the database 126 via thedatabase server 124. For example, the communication engine 205 mayreceive a search query from the client device 108 and, responsive toreceiving the search query, accesses a plurality of items and itemcategories contained within the database 126.

The analysis engine 210 operates to determine whether session data froma plurality of user sessions includes, within the metadata of thesession data, an indicator that a user session includes a submission ofa search query by a past client device. The scoring engine 215 operatesto determine transition scores for items and categories based on browsepatterns of past users for a given query, according to some exampleembodiments. The ranking engine 220 operates to rank items or itemcategories according to the transition score assigned by the scoringengine 215. The display engine 225 is operable to select items or itemcategories based on the ranking by the ranking engine 220. The displayengine 225 also creates a display (e.g., code comprising descriptivetext, markup language, and links to images) of items and item categoriesfor transmission to the client device 108 and presentation on a userinterface on the client device 108.

FIG. 3 is a block diagram illustrating an example software architecture306, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 3 is a non-limiting example of asoftware architecture and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 306 may execute on hardwaresuch as machine 400 of FIG. 4 that includes, among other things,processors 404, memory 414, and I/O components 418. A representativehardware layer 352 is illustrated and can represent, for example, themachine 400 of FIG. 4. The representative hardware layer 352 includes aprocessing unit 354 having associated executable instructions 304. Theexecutable instructions 304 represent the executable instructions of thesoftware architecture 306, including implementation of the methods,components, and so forth described herein. The hardware layer 352 alsoincludes memory and/or storage modules memory/storage 356, which alsohave executable instructions 304. The hardware layer 352 may alsocomprise other hardware 358.

In the example architecture of FIG. 3, the software architecture 306 maybe conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 306 mayinclude layers such as an operating system 302, libraries 320,applications 316 and a presentation layer 314. Operationally, theapplications 316 and/or other components within the layers may invokeapplication programming interface (API) API calls 308 through thesoftware stack and receive a response as in response to the API calls308. The layers illustrated are representative in nature and not allsoftware architectures have all layers. For example, some mobile orspecial purpose operating systems may not provide aframeworks/middleware 318, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 302 may manage hardware resources and providecommon services. The operating system 302 may include, for example, akernel 322, services 324 and drivers 326. The kernel 322 may act as anabstraction layer between the hardware and the other software layers.For example, the kernel 322 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 324 may provideother common services for the other software layers. The drivers 326 areresponsible for controlling or interfacing with the underlying hardware.For instance, the drivers 326 include display drivers, camera drivers,Bluetooth® drivers, flash memory drivers, serial communication drivers(e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers, audiodrivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 320 provide a common infrastructure that is used by theapplications 316 and/or other components and/or layers. The libraries320 provide functionality that allows other software components toperform tasks in an easier fashion than to interface directly with theunderlying operating system 302 functionality (e.g., kernel 322,services 324, and/or drivers 326). The libraries 320 may include systemlibraries 344 (e.g., C standard library) that provide functions such asmemory allocation functions, string manipulation functions, mathematicalfunctions, and the like. In addition, the libraries 320 may include APIlibraries 346 such as media libraries (e.g., libraries to supportpresentation and manipulation of various media format such as MPREG4,H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., an OpenGLframework that may be used to render 2D and 3D in a graphic content on adisplay), database libraries (e.g., SQLite that may provide variousrelational database functions), web libraries (e.g., WebKit that mayprovide web browsing functionality), and the like. The libraries 320 mayalso include a wide variety of other libraries 348 to provide many otherAPIs to the applications 316 and other software components/modules.

The frameworks frameworks/middleware 318 (also sometimes referred to as“middleware”) provides a higher-level common infrastructure that may beused by the applications 316 and/or other software components/modules.For example, the frameworks/middleware 318 may provide various graphicuser interface (GUI) functions, high-level resource management,high-level location services, and so forth. The frameworks/middleware318 may provide a broad spectrum of other APIs that may be utilized bythe applications 316 and/or other software components/modules, some ofwhich may be specific to a particular operating system or platform.

The applications 316 include built-in applications 338 and/orthird-party applications 340. Examples of representative built-inapplications 338 may include, but are not limited to, a contactsapplication, a browser application, a book reader application, alocation application, a media application, a messaging application,and/or a game application. Third-party applications 340 may include anyan application developed using the ANDROID™ or IOS™ software developmentkit (SDK) by an entity other than the vendor of the particular platform,and may be mobile software running on a mobile operating system such asIOS™, ANDROID™, WINDOWS® Phone, or other mobile operating systems. Thethird-party applications 340 may invoke the API calls 308 provided bythe mobile operating system (such as operating system 302) to facilitatefunctionality described herein.

The applications 316 may use built in operating system functions (e.g.,kernel 322, services 324, and/or drivers 326), libraries 320, andframeworks/middleware 318 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systemsinteractions with a user may occur through a presentation layer, such aspresentation layer 314. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

Some software architectures use virtual machines. In the example of FIG.3, this is illustrated by a virtual machine 310. The virtual machine 310creates a software environment where applications/components can executeas if they were executing on a hardware machine (such as the machine 400of FIG. 4, for example). The virtual machine 310 is hosted by a hostoperating system (operating system (OS) 336 in FIG. 3) and typically,although not always, has a virtual machine monitor 360, which managesthe operation of the virtual machine as well as the interface with thehost operating system (i.e., operating system 302). A softwarearchitecture executes within the virtual machine 310 such as anoperating system operating system (OS) 336, libraries 334, frameworks332, applications 330 and/or presentation layer 328. These layers ofsoftware architecture executing within the virtual machine 310 can bethe same as corresponding layers previously described or may bedifferent.

FIG. 4 is a block diagram illustrating components of a machine 400,according to some example embodiments, able to read instructions from amachine-storage medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 4 shows a diagrammatic representation of the machine400 in the example form of a computer system, within which instructions410 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 400 to perform any one ormore of the methodologies discussed herein may be executed. As such, theinstructions 410 may be used to implement modules or componentsdescribed herein. The instructions 410 transform the general,non-programmed machine into a particular machine programmed to carry outthe described and illustrated functions in the manner described. Inalternative embodiments, the machine 400 operates as a standalone deviceor may be coupled (e.g., networked) to other machines. In a networkeddeployment, the machine 400 may operate in the capacity of a servermachine or a client machine in a server-client network environment, oras a peer machine in a peer-to-peer (or distributed) networkenvironment. The machine 400 may comprise, but not be limited to, aserver computer, a client computer, a personal computer (PC), a tabletcomputer, a laptop computer, a netbook, a set-top box (STB), a personaldigital assistant (PDA), an entertainment media system, a cellulartelephone, a smart phone, a mobile device, a wearable device (e.g., asmart watch), a smart home device (e.g., a smart appliance), other smartdevices, a web appliance, a network router, a network switch, a networkbridge, or any machine capable of executing the instructions 410,sequentially or otherwise, that specify actions to be taken by machine400. Further, while only a single machine 400 is illustrated, the term“machine” shall also be taken to include a collection of machines thatindividually or jointly execute the instructions 410 to perform any oneor more of the methodologies discussed herein.

The machine 400 may include processors 404 comprising cores 408 and 412,memory memory/storage 406, and I/O components 418, which may beconfigured to communicate with each other such as via a bus 402. Thememory/storage 406 may include a memory 414, such as a main memory, orother memory storage, and a storage unit 416, both accessible to theprocessors 404 such as via the bus 402. The storage unit 416 and memory414 store the instructions 410 embodying any one or more of themethodologies or functions described herein. The instructions 410 mayalso reside, completely or partially, within the memory 414, within thestorage unit 416, within at least one of the processors 404 (e.g.,within the processor's cache memory), or any suitable combinationthereof, during execution thereof by the machine 400. Accordingly, thememory 414, the storage unit 416, and the memory of processors 404 areexamples of machine-readable media.

The I/O components 418 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 418 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components 418may include many other components that are not shown in FIG. 4. The I/Ocomponents 418 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 418 mayinclude output components 426 and input components 428. The outputcomponents 426 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 428 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 418 may includebiometric components 430, motion components 434, environmentalenvironment components 436, or position components 438 among a widearray of other components. For example, the biometric components 430 mayinclude components to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebio-signals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 434 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environment components 436 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometer that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position components 438 mayinclude location sensor components (e.g., a Global Position System (GPS)receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 418 may include communication components 440 operableto couple the machine 400 to a network 432 or devices 420 via coupling422 and coupling 424 respectively. For example, the communicationcomponents 440 may include a network interface component or othersuitable device to interface with the network 432. In further examples,communication components 440 may include wired communication components,wireless communication components, cellular communication components,Near Field Communication (NFC) components, Bluetooth® components (e.g.,Bluetooth® Low Energy), Wi-Fi® components, and other communicationcomponents to provide communication via other modalities. The devices420 may be another machine or any of a wide variety of peripheraldevices (e.g., a peripheral device coupled via a Universal Serial Bus(USB)).

Moreover, the communication components 440 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components processors communication components 440 mayinclude Radio Frequency Identification (RFID) tag reader components, NFCsmart tag detection components, optical reader components (e.g., anoptical sensor to detect one-dimensional bar codes such as UniversalItem Code (UPC) bar code, multi-dimensional bar codes such as QuickResponse (QR) code, Aztec code, Data Matrix, Dataglyph, MaxiCode,PDF417, Ultra Code, UCC RSS-2D bar code, and other optical codes), oracoustic detection components (e.g., microphones to identify taggedaudio signals). In addition, a variety of information may be derived viathe communication components 440, such as, location via InternetProtocol (IP) geo-location, location via Wi-Fi® signal triangulation,location via detecting a NFC beacon signal that may indicate aparticular location, and so forth.

FIG. 5 shows a flow diagram of a method 500 for generating dissimilarbut relevant search results for a particular category, according to someexample embodiments. At operation 502, the communication engine 205receives an indicator that a search query has been submitted during thecurrent user session. The indicator may comprise a request from theclient device 108 that is submitted over the network 110 and received bythe application server 122. A search query may include a string of text,an image, or other content that is submitted to generate search results.

At operation 504, responsive to receiving the indicator (e.g., thequery), the communication engine 205 accesses session data generatedfrom a plurality of user sessions. The session data may be located atthe database 126, and the communication engine 205 accesses the sessiondata over the database server 124 using a search scheme such as ApacheLucene®. The session data includes a plurality of user sessions, eachuser session representing historical browse data of a past client deviceinteracting with the application server 122 over the network 110 usingthe application 104. For example, a past user may have submitted a querywith the text “smartwatch.” After the results were returned, the usermay have viewed the results and navigated to a listing page for asmartwatch case. The interactions of submitting the smartwatch query,viewing or scrolling through the results, and selecting a smartwatchcase listing can be stored as session data for that past user. In someexample embodiments, the session data is from other users that have acurrently active session or recently terminated session (e.g., lasthour, last day). In this way, the historical data being processed can beused to find recent trends in search result navigation. In other exampleembodiments, the session data is from all users that use the websiteover the past year. In this way, through using longer or olderhistorical session data, more consistent trends in search resultnavigation can be used for score calculation, which discussed in furtherdetail below.

At operation 506, the analysis engine 210 identifies a plurality of usersessions from the session data accessed by the communication engine 205.The analysis engine 210 identifies the plurality of user sessions bydetecting which of the user sessions within the accessed session datahas matching query data (e.g., matching or similar query strings). Forinstance, the communication engine 205 receives an indicator that asearch query has been submitted to the application server 122 by theclient device 108. The indicator could, for example, include the text ofa textual query within the metadata of the indicator. For example, ifthere is a submission from the client device 108 of a query for string“Nike,” the indicator received by the communication engine 205 includesthe text “Nike” in the metadata of the indicator. Responsive toreceiving the indicator the communication engine 205 accesses aplurality of user sessions containing session data. The analysis engine210 then determines which user sessions from the accessed session datainvolved queries using the text “Nike.” In some example embodiments, theanalysis engine 210 implements string schemes (e.g., Levenshteindistance algorithm) to determine which user sessions correlate toqueries having similar query terms.

At operation 508, the communication engine 205 accesses a plurality ofitem category datasets, each item category dataset comprising aplurality of items. For example, a shoe category dataset comprises itemdata for different shoes, and a smartwatch category dataset comprisesitem data for different smartwatches. The plurality of item categorydatasets and the items within these categories may be accessed by thecommunication engine 205 from the database 126 via the database server124. The item categories may be designated based on aspects (e.g. brand,color, pattern, shape) or based on utility (e.g. capability, itemspecification). In some example embodiments, the categories may furtherinclude condition of the item, shipping location of the item, salemethod (e.g. auction, close out), and other categories not listedherein. Each item within each of the categories includes an item page(e.g., a listing webpage, mobile app page) published via the publicationsystem 106. The client device 108 can use the application 104 to causethe display of a user interface of images of the item, or take otheraction (e.g., submit an order for the item, bid on the item, bookmarkthe item).

At operation 510, the scoring engine 215 determines an transition scorebased on the quantity of user sessions within the plurality of usersessions that correspond to users navigating to a given item page. Forexample, the user sessions can include, within the metadata of thesession data included in the user session, an indication that the clientdevice 108 navigated to the item page associated with the item aftersubmitting the search query. The scoring engine 215 then determines atransition score based on how many of the user sessions within theplurality of user sessions navigated to the given item page. Forexample, if 4,390 out of 62,026 user sessions within the plurality ofuser sessions navigate to the item page, the scoring engine 215determines that the score for the item page is 7.07, based on thecalculation that 7.07% of the previous user sessions navigated to theitem page after inputting the search query. Thus, the scoring engine 215assigns a score of between zero and 100 to every item page, according tosome example embodiments. In some example embodiments, the itemtransition score is an integer representing the users that clicked on acertain item page. For example, if 4390 out of 62,026 user sessionsclicked on a given item page, the 4390 integer is assigned to the itempage as its item transition score.

Continuing with the previous example involving the “Nike,” thecommunication engine 205 accesses the database 126 via the databaseserver 124, the database 126 containing a plurality of items, each itemhaving an item page. The scoring engine 215 then determines a score foreach of the available item pages based on a quantity of user sessionsthat have browse data that indicate user's submitted the query “Nike”and subsequently navigated to the item page using the application 104.

At operation 512, the ranking engine 220 ranks items within theplurality of items based on the transition score determined by thescoring engine 215 in operation 510. Thus, the items within theplurality are ordered by the ranking engine 220 starting with the itemthat past client devices have historically most frequently navigated toafter submitting the query containing a given query term.

At operation 514, the display engine 225, generates a display based onthe ranking of the items within the plurality, the display beingpresentable on a user interface on the client device 108. According tosome example embodiments, the display engine 225 selects a top segmentof the items within the plurality, the top segment being a number ofitems that is ordered first by the ranking engine 220. The display mayinclude a presentation of images representing each of the top segment ofthe items.

At operation 516, the display engine 225 transmits the display (e.g.,markup language comprising item listing and image data) over the network110 to the application 104 on the client device 108. The display maythen be viewable on a user interface on the client device 108.

Continuing the above example with the “Nike” query, the ranking engine220 orders the items within the plurality according to the transitionscore determined by the scoring engine 215. The display engine 225selects the top 20 items with the highest transition scores andgenerates a display including images of these top 20 items. Finally, thedisplay engine 225 transmits the display to the client device 108 overthe network 110. The display is then rendered and made viewable on theclient device 108 (e.g., on a user interface).

FIG. 6 illustrates a flow diagram of a method 600 for improved diversitysearch results based at least in part on categorical rankings, accordingto some example embodiments. As illustrated, the method 600 can beperformed after the method 500. As such, operation 602 corresponds tooperation 516, according to some example embodiments. At operation 604,the scoring engine 215 determines a category transition score for theitem category based on the item transition scores of the plurality ofitems within the given category. For example, if a the query “Nike” isreceived, the scoring engine 215 aggregates the item transition scoresfor a given category to generate a category transition score. In someexample embodiments, the item transition scores of a given category areadded together to generate the category transition score for thatcategory. In some example embodiments, the item transitions scores of agiven category are averaged to yield the category transition score forthat given category.

At operation 606, the ranking engine 220 orders item categories againstother item categories based on their respective category transitionscores. At operation 608, the display engine 225 alters the presentationof the display based on the ranking of the categories. In an exampleembodiment, the display engine 225 may order an entire item category tobe displayed before another item category. For example, for the query“Nike,” the item category “men's running shoes” may be displayed higherin the search results than the category for “women's watches” based onthe ranking of the categories.

At operation 610, the display engine 225 transmits the altered displayover the network 110 to the client device 108 for display on the userinterface by the application 104. Although the example described hereinalters the display generated using method 500 in FIG. 5, one of ordinaryskill in the art will appreciate that, according to some exampleembodiments, the items are ranked (e.g., operations 510 and 512 in FIG.5) in each category, then the categories are ranked against each other(e.g., operations 604 and 606 in FIG. 6). Then, after the categories areranked, and the items within each categories are ranked, a display isgenerated displaying the ranked categories, each of which comprises aplurality of ranked items.

FIG. 7 illustrates a flow diagram of a method 700 for improved diversitysearch results based at least in part on diversity rankings, accordingto some example embodiments. As illustrated, the method 700 can beperformed after the method 600. As such, operation 702 corresponds tooperation 610, according to some example embodiments. At operation 704,the scoring engine 215 determines a diversity score between a first itemcategory and a second item category by determining similarities betweenthe items in the respective categories. The first item category andsecond item category may be item categories that have already beenselected for inclusion in the search results based on the operations ofmethod 500 and 600, as described above.

Similar features for items include similar physical aspects (e.g. shape,color) of the items as well as similar purposes or functions. In someembodiments, the scoring engine 215 assigns a higher diversity score tothe second item category based on the first item category and the seconditem category having few or no similar features between them. This mayindicate that even though the second category contains potentially verydifferent types of items than what was initially searched for, usersnonetheless navigate to web pages of items in the second category. Inthis way, the browse habits of other users can improve the userexperience (e.g., search result presentation) of a particular user. Insome example embodiments, the scoring engine 215 implements a machineclassifier (e.g., support vector machine, neural networks, decisiontrees, ensemble learning schemes) trained on database data to generatediversity scores for given categories. The database data can include allitem and category data stored in the database 126 for use by thepublication system 106.

As an example, the scoring engine 215 may determine that the itemcategory “Men's Running Shoes” and the item category “Men's Apparel”have a diversity score of 45 between them. Conversely, the scoringengine 215 may determine that the item category “Men's Running Shoes”and the item category “Men's Cross-training Shoes” of the diversityscore of 5 between them. The higher diversity score is due to the itemsin “Men's Apparel” having less shared features with the “Men's Runningshoes” as compared to the items in “Men's Cross-training Shoes” and theitems in “Men's Running shoes”.

At operation 706, the ranking engine 220 ranks the second item categorybased on the diversity score assigned by the scoring engine 215. Theranking engine 220 can order the second item category, among othercategories, according to the diversity score of the second itemcategory. The ranking engine 220 further merges the category transitionscore for a given item category with the diversity score of the givencategory. For example, if a first item category is ranked lower than,but within 5 points of a second item category and has a higher diversityscore with a third displayed category than the second item category, theranking engine 220 may rank the first item category higher despite thelower transition score. Thus, the ranking engine 220 factors indiversity amongst item categories when ranking item categories.

At operation 708, the display engine 225 alters the display based on theranking of the second item category. For example, if the merged rankingdescribed above has been altered by the inclusion of the diversity scorein the second item category and the ordering of item categories based ondiversity score, the display engine 225 may select one or more newcategories to display that are now ranked ahead of the previouslydisplayed categories.

At operation 710, the display engine 225 transmits the altered displayto the client device 108 over the network 110. The altered display isfurther presentable on the client device 108 on a user interface.

In some example embodiments, the diversity search engine 128 isconfigured to increase diversity by selecting the top N categories ofthe class that has the highest category transition score, then selectingthe top N categories of the second most popular class of categoriesaccording to category transition scores.

The transition score and, by extension, the item transition score andthe second transition score, may further be influenced by additionalfactors, according to some example embodiments. For example, the scoringengine 215 may raise or lower the first transition score, as it appliesto the item, or the second transition score, as it applies to an itemcategory, based on an average time it has taken for past client devicesto navigate to an item or an item within the item category from theinitial search query. For example, if past client devices on averagetake one minute to navigate to “Nike swoosh T-shirt” after searching for“Nike shoes,” the scoring engine 215 may assign a higher item transitionscore to “Nike swoosh T-shirt” than if the past client devices havetaken 30 minutes on average. Similarly, assuming the item “Nike swooshT-shirt” is in the item category “Men's Apparel,” the scoring component212 may assign a similar category transition score to “Men's Apparel.”

Similarly, the scoring engine 215 may base the item transition score orthe category transition score on an average number of pages that pastclient devices navigate through (after inputting the search query)before navigating to a particular item or item category. Continuing theabove example, the scoring engine 215 may assign a higher transitionscore to “Nike swoosh T-shirt” if past client devices had only navigatedthrough an average of three item pages after submitting the query “Nikeshoes” than the past client device having navigated through an averageof 25 item pages.

Additionally, the scoring engine 215 may base the item transition scoreor the category transition score off whether the search query wasfiltered during the search and, if the search query was filtered, howmany times it was filtered. This filtration information may be locatedwithin the session data of the past historical user sessions. Forexample, the scoring engine 215 may assign a higher item transitionscore to “Nike swoosh T-shirt” if past client devices had not typicallyinput any other search query aside from “Nike shoes.” Additionally, thescoring engine 215 may assign a higher item transition score for thepast client devices having filtered “Nike shoes” to “Nike running shoes”rather than “Nike shoes” to “Nike men's apparel.” Thus, the scoringengine 215 is able to prioritize items and item categories that are moreparticular to the original search query.

Finally, the scoring engine 215 may base the item transition score orthe category transition score off whether a purchase event has occurredsubsequent to the submission of the search query and the navigation ofthe past client devices to the item page or to the item category. Forexample, the scoring engine 215 may assign a lower item transition scoreif the user has purchased the item after using the search query butbefore navigating to the item that is being scored.

FIG. 8 shows an example user interface 800 generated by the diversitysearch engine 128, according to some example embodiments. Asillustrated, the search results can comprise a first item category 805,a second item category 810, a third item category 815, and a fourth itemcategory 820, and so on. Each item category comprises items, each ofwhich comprises an image thumbnail and descriptive text. Each of theitems illustrated on the user interface 800 may link to a page (e.g.,webpage) that displays the item in listing format. As discussed above,the listings can be published to the publication system 106 and storedin the database 126. As an illustrative example, assume a user through aclient device submitted a query comprising the text “Nike”. In responseto the submitted query, the operations of FIG. 5 may return matchingitems (e.g., items that are Nike related, clothing, shoes, watches), andfurther generate a transition score for each of the items based on thehistorical data as discussed above. Thus, for example, the items in thefirst item category 805 (Men's Athletic Shoes) can be ordered as shown,from right to left, the right most item having the highest transitionscore.

In further response to the query, the operations of FIG. 6 may orderentire categories according to the highest category transition scores,from top to bottom in the user interface 800, where the category withthe highest transition score is placed at the top of the search resultson the user interface 800.

In further response to the query, the operations of FIG. 7 may order thecategories according to diversity scores. For example, assume that afterthe operations of FIG. 6, the third item category 815 and the fourthitem category 820 have the same category transition scores. However,after the diversity determinations of the operations of FIG. 7, thethird item category 815 has a higher overall score (e.g., merged score)and thus is placed higher than the fourth item category 820 in thesearch results. The higher diversity score of the third item category815 (e.g., a category for women's clothing) may be with respect to thehighest ranked item category, which is the first item category 805(e.g., a category for men's shoes).

Further, as mentioned above, in some example embodiments, the diversitysearch engine 128 selects the most popular (according to categorytransition scores) N categories of class for the top portion of thesearch results, and the next most popular N categories for the nextportion of the search results, and so forth. For example, with referenceto FIG. 8, assume the scoring engine 215 determines that the first itemcategory 805 has the highest category transition score according tohistorical user data. The first item category is of the shoe class ofphysical items tracked by database 126. Accordingly, if N is set to “2”,then the first item category 805 is positioned at the top of the searchresults, and the next highest category of the shoe class (according tocategory transition score) is placed below the second item category 805.In the example shown in FIG. 8, of the categories in the shoe class, thenext highest category is women's running shoes, thus second itemcategory 810, which contains women's running shoes matching the query“Nike” are placed in the second position. In the example where N=2,after the first two categories are positioned, the scoring engine 215then determines: of the categories, what is the next highest classexcluding the already used class (e.g., the shoes class). Assume thenext highest category according to category transition score is theapparel class. Accordingly, the scoring engine 215 selects the highestranking category (according to category transition score) within thatclass, which is third item category 815, and places third item category815 in the third position in the search results. Similarly, the nexthighest category (according to category transition score) within theapparel class is selected and placed. Thus, as illustrated, the fourthitem category 820 is selected and placed as the fourth position. Othercategories and items may similarly be placed below the fourth positionon subsequent pages or in a scrollable area (not depicted) below thefourth position in FIG. 8.

What is claimed is:
 1. A method comprising: in response to receiving aquery from a client device: grouping a plurality of items identified assearch results for the query into a plurality of categories of items;generating a diversity score for each category for at least a portion ofthe categories, the diversity score for each category based on acomparison of items in the category with items in at least one othercategory from the plurality of categories; and causing presentation of auser interface with the search results for the query, the user interfacedisplaying the plurality of items with items in each category groupedtogether by category, wherein the categories are presented on the userinterface relative to one another at least in part according to thediversity scores.
 2. The method of claim 1, wherein the plurality ofitems are organized, in a database, into the categories of items,wherein the items of a particular category share at least one attributethat describes the particular category.
 3. The method of claim 1,further comprising: generating category transition scores for at least aportion of the categories of items.
 4. The method of claim 3, furthercomprising: ranking the categories of items at least in part accordingto the category transition scores; and wherein the user interfacedisplays the plurality of items in the categories of items at least inpart according to the category transition scores.
 5. The method of claim3, wherein generating diversity scores comprises comparing a category ofitems having a highest category transition score to one or more othercategories of items.
 6. The method of claim 5, wherein the userinterface displays the category having the highest category transitionscore followed by the one or more other categories of items orderedaccording to the diversity scores.
 7. The method of claim 1, furthercomprising: generating category transition scores for the categories ofitems; ranking the categories of items according to the categorytransition scores; re-ranking the categories of items according to thediversity scores; and wherein the user interface displays the pluralityof items in the categories of items ranked according to the categorytransition scores and the diversity scores.
 8. The method of claim 7,wherein the categories of items are re-ranked by merging the categorytransition scores with the diversity scores.
 9. The method of claim 3,wherein the category transition score for a given category is based onitem transition scores for items within the category determined usinghistorical user data regarding past user interactions with the items.10. The method of claim 9, wherein the item transition scores arefurther based on an average number of pages viewed during a given usersession from the historical user data, the viewing of pages occurringbetween submission of a query submitted in the given user session andnavigation to a page of an item during the given user session.
 11. Themethod of claim 9, wherein the item transition scores are further basedon: an average number of times a given query of a given user session,from the historical user data, was filtered, the filtering of the givenquery occurring between submission of the given query and navigation toa page of an item during the given user session; and/or an average timespan between submission of a given query of a given user session, fromthe historical user data, to a page of the item.
 12. The method of claim1, wherein the diversity score for a first category is generated bycomparing features of items in the first category with features of itemsin a second category.
 13. A system comprising: one or more processors ofa machine; and a memory storing instructions that, when executed by theone or more processors, cause the machine to perform operationscomprising: in response to receiving a query from a client device:grouping a plurality of items identified as search results for the queryinto a plurality of categories of items; generating a diversity scorefor each category for at least a portion of the categories, thediversity score for each category based on a comparison of items in thecategory with items in at least one other category from the plurality ofcategories; and causing presentation of a user interface with the searchresults for the query, the user interface displaying the plurality ofitems with items in each category grouped together by category, whereinthe categories are presented on the user interface relative to oneanother at least in part according to the diversity scores.
 14. Thesystem of claim 13, wherein the plurality of items are organized, in adatabase, into the categories of items, wherein the items of aparticular category share at least one attribute that describes theparticular category.
 15. The system of claim 13, the operations furthercomprising: generating category transition scores for at least a portionof the categories of items.
 16. The system of claim 15, the operationsfurther comprising: ranking the categories of items at least in partaccording to the category transition scores; and wherein the userinterface displays the plurality of items in the categories of items atleast in part according to the category transition scores.
 17. Thesystem of claim 14, wherein generating diversity scores comprisescomparing a category of items having a highest category transition scoreto one or more other categories of items.
 18. The system of claim 17,wherein the user interface displays the category having the highestcategory transition score followed by the one or more other categoriesof items ordered according to the diversity scores.
 19. The system ofclaim 13, the operations further comprising: generating categorytransition scores for the categories of items; ranking the categories ofitems according to the category transition scores; re-ranking thecategories of items according to the diversity scores; and wherein theuser interface displays the plurality of items in the categories ofitems ranked according to the category transition scores and thediversity scores.
 20. A machine storage device embodying instructionsthat, when executed by a machine, cause the machine to performoperations comprising: in response to receiving a query from a clientdevice: grouping a plurality of items identified as search results forthe query into a plurality of categories of items; generating adiversity score for each category for at least a portion of thecategories, the diversity score for each category based on a comparisonof items in the category with items in at least one other category fromthe plurality of categories; and causing presentation of a userinterface with the search results for the query, the user interfacedisplaying the plurality of items with items in each category groupedtogether by category, wherein the categories are presented on the userinterface relative to one another at least in part according to thediversity scores.